基于双生成器生成对抗网络的电力系统暂态稳定评估方法  

Transient Stability Assessment of Power System Based on DGL-GAN

作　　者：杨东升[1] 吉明佳 周博文 卜思齐 胡博 YANG Dongsheng;JI Mingjia;ZHOU Bowen;BU Siqi;HU Bo(College of Information Science and Engineering,Northeastern University,Shenyang 110819,Liaoning Province,China;Department of Electrical Engineering,The Hongkong Polytechnic University,Jiulong District,Hongkong 999077,China;State Grid Liaoning Electric Power Company Limited,Shenyang 110004,Liaoning Province,China)

机构地区：[1]东北大学信息科学与工程学院,辽宁省沈阳市110819 [2]香港理工大学电机工程系,香港九龙区999077 [3]国网辽宁省电力有限公司,辽宁省沈阳市110004

出　　处：《电网技术》2021年第8期2934-2944,共11页Power System Technology

基　　金：国家自然科学基金资助项目(U1908217);辽宁省“兴辽英才计划”资助项目(XLYC1902055,XLYC1902090);中央高校基本科研项目(N180415004)。

摘　　要：当前采用深度学习网络实现电力系统暂态稳定评估,由于样本多样性不足,抗干扰性差等问题导致评估算法的分类性能受到很大的影响。针对上述问题提出了一种基于双生成器生成对抗网络(double generator LSTM-generative adversarial network,DGL-GAN)的暂态稳定评估方法。DGL-GAN中批量样本生成器与判别器构成对抗网络,通过交替训练学习暂态数据的分布特性,批量生成符合真实分布的新样本,解决样本多样性不足的问题;修复生成器由LSTM自编码器构成,其作用不但可以去除电力系统暂态数据中的噪声而且可以补偿仿真或量测缺失的片段,解决评估算法抗干扰能力差的问题。此外,提出的基于多层LSTM的网络结构设计可以进一步提高模型对暂态时序数据的特征提取能力。IEEE-39节点系统仿真结果表明:所提方法能够有效增强样本多样性,显著提升暂态稳定评估性能,同时还使得模型具有良好的抗干扰能力。When the deep learning network is used to realize the transient stability assessment of the power system,the classification performance of the assessment algorithm is greatly affected due to the insufficient sample diversity and the poor anti-interference.In view of the above problems,this paper proposes a transient stability assessment method based on the double generator LSTM-generative adversarial network(DGL-GAN).In this method,on the one hand,the batch sample generator and the discriminator form an adversarial network which generates new samples in batches that match the true distribution through alternate training to learn the distribution characteristics of transient data,thus solving the problem of insufficient sample diversity in power system transient assessment.On the other hand,the repair generator has an LSTM autoencoder which can not only remove the noise in the transient data of the power system but also compensate for the missing fragments in simulation or measurement,solving the problem of poor anti-interference ability of the evaluation algorithm.In addition,the network structure design proposed in this paper is based on multi-layer LSTM which can further improve the model's feature extraction ability of the transient time series data.The simulation results in the New England IEEE 39-bus system show that the transient stability assessment model proposed in this paper can effectively enhance the sample diversity,significantly improve the transient stability assessment performance,and also have good anti-interference ability.

关 键 词：电力系统 暂态稳定评估 生成对抗网络 长短期记忆网络 

分 类 号：TM721[电气工程—电力系统及自动化]